Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks
Authors: Kesen Zhao, Liang Zhang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments have been conducted on both synthetic and real-world datasets, where our approach yields substantial improvements, thereby demonstrating significant superiority. |
| Researcher Affiliation | Academia | Kesen Zhao City University of Hong Kong Hong Kong, China EMAIL Liang Zhang Shenzhen Research Institute of Big Data Guangdong, China EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code and the dataset benchmarks are available 1https://github.com/kesenzhao/DyGNNExplainer |
| Open Datasets | Yes | Elliptic 2. http://www.kaggle.com/ellipticco/elliptic-data-set ... The code and the dataset benchmarks are available 1https://github.com/kesenzhao/DyGNNExplainer |
| Dataset Splits | Yes | We divide the dataset as training set and test set with a ratio of 8:2, which is a common setting in previous works. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA Tesla V100S GPU |
| Software Dependencies | No | Only the Adam optimizer was mentioned without a specific version number. No other software dependencies with version numbers were provided. |
| Experiment Setup | Yes | For the VGAE, we apply a two-layer GCN with output dimensions [32, 64, 128] and [16, 32, 64] in the encoder. The max time step T is set as 5. In the contrastive loss, the temperature coefficient τ, weight parameters α1 and α2 are set from [0.2, 0.5, 0.8]. In the final optimization objects, the loss function weight parameters λ1, λ2, λ3, and λ4 are set from [0.2, 0.4, 0.6, 0.8, 1]. And the best performance is obtained where λ1 = 1, λ2 = 0.4, λ3 = 0.2, and λ4 = 0.2. We trained the explainers using the Adam optimizer (Kingma & Ba, 2014) with a learning rate of [1e-2, 1e-3, 1e-4] and batch size 64. |